• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.08a
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• pp.5-8
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• 2000

This paper introduced a scheme for finding an optimal association matrix that represents the relationships between the measurements and tracks in multi-target tracking of Radar system. We considered the relationships between targets and measurements as MRF and assumed a priori of the associations as a Gibbs distribution. Based on these assumptions, it was possible to reduce the MAP estimate of the association matrix to the energy minimization problem. After then, we defined an energy function over the measurement space, that may incorporate most of the important natural constraints.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.5
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• pp.54-63
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• 2007

The purpose of this study is to present a new energy minimization method for image completion with hierarchical approach. The goal of image completion is to fill in missing part in a possibly large region of an image so that a visually plausible outcome is obtained. An exemplar-based Markov Random Field Modeling(MRF) is proposed in this paper. This model can deal with following problems; detection of global features, flexibility on environmental changes, reduction of computational cost, and generic extension to other related domains such as image inpainting. We use the Priority Belief Propagation(Priority-BP) which is a kind of Belief propagation(BP) algorithms for the optimization of MRF. We propose the hierarchical Priority-BP that reduces the number of nodes in MRF and to apply hierarchical propagation of messages for image completion. We show that our approach which uses hierarchical Priority-BP algorithm in image completion works well on a number of examples.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.2
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• pp.1-9
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• 2009

In recent years, a film restoration has gained increasing attention by many researchers, to support multimedia service of high quality. In general, an old film is degraded by dust, scratch, flick, and so on. Among these, the common factors are scratch and blotch, so that many researchers have been investigated to restore these degradations. However, the methods in literature have one major limitation: A method is working well in dealing with scratches, however it is poorly working in processing the blotches. The goal of this work is to develop a robust technique to restore images degraded by both scratches and blotches. For this, we use MRF-MAP (Markov random field - maximum a posteriori) framework, so that the restoration problem is considered as the minimization problem of the posteriori energy function. As the minimization is one of complex combinatorial problem, we use distributed genetic algorithms (DGAs) that effectively deal with combinatorial problems. To asses the validity of the proposed method, it was tested on natural old films and artificially degraded films, and the results were compared with other methods. Then, the results show that the proposed method is superior to other methods.

• Journal of the Institute of Convergence Signal Processing
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• v.1 no.2
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• pp.138-144
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• 2000

This paper introduced a scheme for finding an optimal association matrix that represents the relationships between the measurements and tracks in multi-target tracking of Radar system. We considered the relationships between targets and measurements as MRF and assumed a priori of the associations as a Gibbs distribution. Based on these assumptions, it was possible to reduce the MAP estimate of the association matrix to the energy minimization problem. After then, we defined an energy function over the measurement space, that may incorporate most of the important natural constraints.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.148-153
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• 2009

Few methods have dealt with segmenting multiple images with analogous content. Concurrent images of a scene and gathered images of a similar foreground are examples of these images, which we term consistent scene images. In this paper, we present a method to segment these images based on manual segmentation of one image, by iteratively propagating information via multi-level cues with adaptive confidence. The cues are classified as low-, mid-, and high- levels based on whether they pertain to pixels, patches, and shapes. Propagated cues are used to compute potentials in an MRF framework, and segmentation is done by energy minimization. Through this process, the proposed method attempts to maximize the amount of extracted information and maximize the consistency of segmentation. We demonstrate the effectiveness of the proposed method on several sets of consistent scene images and provide a comparison with results based only on mid-level cues [1].

• Journal of Broadcast Engineering
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• v.13 no.5
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• pp.685-695
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• 2008

This paper presents a method to segment the object of interest from a set of multi-view images with minimal user interaction. Specifically, after the user segments an initial image, we first estimate the transformations between foreground and background of the segmented image and the neighboring image, respectively. From these transformations, we obtain regions in the neighboring image that respectively correspond to the foreground and the background of the segmented image. We are then able to segment the neighboring image based on these regions, and iterate this process to segment the whole image set. Transformation of foregrounds are estimated by feature-based registration with free-form deformation, while transformation of backgrounds are estimated by homography constrained to affine transformation. Here, both are based on correspondence point pairs. Segmentation is done by estimating pixel color distributions and defining a shape prior based on the obtained foreground and background regions and applying them to a Markov random field (MRF) energy minimization framework for image segmentation. Experimental results demonstrate the effectiveness of the proposed method.

• Journal of Broadcast Engineering
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• v.14 no.4
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• pp.450-462
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• 2009

So far, many methods for segmenting single images or video have been proposed, but few methods have dealt with multiple images with analogous content. These images, which we term consistent scene images, include concurrent images of a scene and gathered images of a similar foreground, and may be collectively utilized to describe a scene or as input images for multi-view stereo. In this paper, we present a method to segment these images with minimum user input, specifically, manual segmentation of one image, by iteratively propagating information via multi-level cues with adaptive confidence depending on the nature of the images. Propagated cues are used as the bases to compute multi-level potentials in an MRF framework, and segmentation is done by energy minimization. Both cues and potentials are classified as low-, mid-, and high- levels based on whether they pertain to pixels, patches, and shapes. A major aspect of our approach is utilizing mid-level cues to compute low- and mid- level potentials, and high-level cues to compute low-, mid-, and high- level potentials, thereby making use of inherent information. Through this process, the proposed method attempts to maximize the amount of both extracted and utilized information in order to maximize the consistency of the segmentation. We demonstrate the effectiveness of the proposed method on several sets of consistent scene images and provide a comparison with results based only on mid-level cues [1].